Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions
Authors
Keywords
-
Journal
Electronics
Volume 11, Issue 7, Pages 1111
Publisher
MDPI AG
Online
2022-04-01
DOI
10.3390/electronics11071111
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- An Architecture of IoT-Aware Healthcare Smart System by Leveraging Machine Learning
- (2022) Hamza Aldabbas et al. International Arab Journal of Information Technology
- Deep learning for depression recognition with audiovisual cues: A review
- (2021) Lang He et al. Information Fusion
- Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm
- (2020) Kyoung-Sae Na et al. NEUROSCIENCE LETTERS
- A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography
- (2020) Xiaowei Li et al. Frontiers in Neuroscience
- Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support Effective ML System Design
- (2020) Anja Thieme et al. ACM Transactions on Computer-Human Interaction
- Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease
- (2020) Lana G. Tennenhouse et al. JOURNAL OF PSYCHOSOMATIC RESEARCH
- Use of machine learning approach to predict depression in the elderly in China: A longitudinal study
- (2020) Dai Su et al. JOURNAL OF AFFECTIVE DISORDERS
- Review of Machine Learning Algorithms for Diagnosing Mental Illness
- (2019) Gyeongcheol Cho et al. Psychiatry Investigation
- Depression recognition using machine learning methods with different feature generation strategies
- (2019) Xiaowei Li et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- A Brief Review of Artificial Intelligence Applications and Algorithms for Psychiatric Disorders
- (2019) Guang-Di Liu et al. Engineering
- A survey on semi-supervised learning
- (2019) Jesper E. van Engelen et al. MACHINE LEARNING
- Implementing machine learning in bipolar diagnosis in China
- (2019) Yantao Ma et al. Translational Psychiatry
- A deep learning framework for automatic diagnosis of unipolar depression
- (2019) Wajid Mumtaz et al. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
- On some aspects of minimum redundancy maximum relevance feature selection
- (2019) Peter Bugata et al. Science China-Information Sciences
- Machine Learning Approaches for Clinical Psychology and Psychiatry
- (2018) Dominic B. Dwyer et al. Annual Review of Clinical Psychology
- Automated depression analysis using convolutional neural networks from speech
- (2018) Lang He et al. JOURNAL OF BIOMEDICAL INFORMATICS
- Relief-Based Feature Selection: Introduction and Review
- (2018) Ryan J. Urbanowicz et al. JOURNAL OF BIOMEDICAL INFORMATICS
- Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants
- (2018) Gandharva Nagpal et al. Journal of Translational Medicine
- Machine learning in major depression: From classification to treatment outcome prediction
- (2018) Shuang Gao et al. CNS Neuroscience & Therapeutics
- Mental health monitoring with multimodal sensing and machine learning: A survey
- (2018) Enrique Garcia-Ceja et al. Pervasive and Mobile Computing
- Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017
- (2018) Spencer L James et al. LANCET
- Analysis of user-generated content from online social communities to characterise and predict depression degree
- (2017) Iram Fatima et al. JOURNAL OF INFORMATION SCIENCE
- Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study
- (2017) Kevin Hilbert et al. Brain and Behavior
- Studying depression using imaging and machine learning methods
- (2016) Meenal J. Patel et al. NeuroImage-Clinical
- Artificial intelligence approach to classify unipolar and bipolar depressive disorders
- (2015) Turker Tekin Erguzel et al. NEURAL COMPUTING & APPLICATIONS
- Stem/Progenitor Cells in Liver Development, Homeostasis, Regeneration, and Reprogramming
- (2014) Atsushi Miyajima et al. Cell Stem Cell
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now